Decomposing the transfer entropy to assess higher order effects in Cardiovascular Interactions*

Transfer Entropy (TE) can exhibit bias-either in deficiency or excess-during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. To handle high order effects, instead of conditioning TE on all the measured processes except the driver and target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, one can search for both the multiplets of variables that maximize information flow and those that minimize it, thus obtaining a decomposition of TE into unique, redundant, and synergistic atoms. This approach quantifies the relative importance of high-order effects compared to pure two-body effects while highlighting the processes that contribute to building these high-order effects alongside the driver. We employ this approach to analyze cardiovascular and cardiorespiratory interactions related to baroreflex and respiratory sinus arrhythma mechanisms.

Authors

Institutions

Publication Details

Published
2024-10-23
DOI
https://doi.org/10.1109/esgco63003.2024.10767000
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decomposing the transfer entropy to assess higher order effects in Cardiovascular Interactions*

Yuri Antonacci, Michal Javorka, Luca Faes, Hélder Pinto et al.
Computational Drug Discovery Methods
article

Decomposing the transfer entropy to assess higher order effects in Cardiovascular Interactions*

Yuri Antonacci, Michal Javorka, Luca Faes, Hélder Pinto, Sebastiano Stramaglia
article en

Abstract

Transfer Entropy (TE) can exhibit bias-either in deficiency or excess-during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. To handle high order effects, instead of conditioning TE on all the measured processes except the driver and target, as in its fully conditioned version, or not conditioning at all, as in the pairwise approach, one can search for both the multiplets of variables that maximize information flow and those that minimize it, thus obtaining a decomposition of TE into unique, redundant, and synergistic atoms. This approach quantifies the relative importance of high-order effects compared to pure two-body effects while highlighting the processes that contribute to building these high-order effects alongside the driver. We employ this approach to analyze cardiovascular and cardiorespiratory interactions related to baroreflex and respiratory sinus arrhythma mechanisms.

Universidade do Porto (PT), Martin University Hospital (SK), University of Bari Aldo Moro (IT), University of Palermo (IT)
Zero hunger
Openalex Percentile: Top 74%
Computational Drug Discovery Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.